COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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TRUE
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FALSE
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Either A or B
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None of the above
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Detailed explanation-1: -Overfitting could be an upshot of an ML expert’s effort to make the model ‘too accurate’. In overfitting, the model learns the details and the noise in the training data to such an extent that it dents the performance.
Detailed explanation-2: -So increasing the amount of data can only make overfitting worse if you mistakenly also increase the complexity of your model. Otherwise, the performance on the test set should improve or remain the same, but not get significantly worse.
Detailed explanation-3: -The overfitting happens when it learns a complex pattern in data or in short it leads to memorisation of the data. If it was memorisation, wouldn’t it show more false negatives as it has only memorised the training data and is unable to detect new cases.
Detailed explanation-4: -Overfitting occurs when the model cannot generalize and fits too closely to the training dataset instead. Overfitting happens due to several reasons, such as: The training data size is too small and does not contain enough data samples to accurately represent all possible input data values.